Center for Medical Image Computing, Department of Computer Science, University College London (UCL), Gower Street, London WC1E 6BT, UK.
J Magn Reson. 2010 Sep;206(1):41-51. doi: 10.1016/j.jmr.2010.05.017. Epub 2010 Jun 1.
Variations in gradient waveforms can provide different levels of sensitivity to microstructure parameters in diffusion-weighted MR. We present a method that identifies gradient waveforms with maximal sensitivity to parameters of a model relating microstructural features to diffusion MR signals. The method optimizes the shape of the gradient waveform, constrained by hardware limits and fixed orientation, to minimize the expected variance of parameter estimates. The waveform is defined discretely and each point optimized independently. The method is illustrated with a biomedical application in which we maximize the sensitivity to microstructural features of white matter such as axon radius, intra-cellular volume fraction and diffusion constants. Simulation experiments find that optimization of the shape of the gradient waveform improves sensitivity to model parameters for both human and animal MR systems. In particular, the optimized waveforms make axon radii smaller than 5 microm more distinguishable than standard pulsed gradient spin-echo (PGSE). The identified class of optimized gradient waveforms have dominant square-wave components with frequency that increases as the radius size decreases.
梯度波形的变化可以为扩散加权磁共振中的微观结构参数提供不同的灵敏度。我们提出了一种方法，该方法可以识别对将微观结构特征与扩散磁共振信号相关联的模型的参数具有最大灵敏度的梯度波形。该方法通过硬件限制和固定方向来优化梯度波形的形状，以最小化参数估计的期望方差。该波形是离散定义的，并且每个点都是独立优化的。该方法通过生物医学应用进行了说明，在该应用中，我们最大程度地提高了对诸如轴突半径，细胞内体积分数和扩散常数之类的白质微观结构特征的灵敏度。仿真实验发现，梯度波形形状的优化提高了对人体和动物磁共振系统模型参数的灵敏度。特别是，与标准脉冲梯度自旋回波（PGSE）相比，优化后的波形使半径小于 5 微米的轴突更易于区分。已识别的一类优化梯度波形具有主导的方波分量，其频率随半径尺寸的减小而增加。
